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Unsupervised Investments (II): A Guide to AI Accelerators and Incubators

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A meticulously compiled list as extensive as possible of every accelerator, incubator or program the author has read or bumped into over the past months. It looks like there are at least 29 of them. An interesting read for a wide variety of potentially interested parties - far beyond only the investor.



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I. Rationale for the post

 
Well, let’s be completely honest: the current startups landscape is incredibly messy. Venture capitalist, angels, incubators, accelerators, private equity funds, corporate venture capital, private companies, research grants. There are plenty of ways to get funded to start your own company — but how many of them are not simply ‘dumb money’? How many of them give you some additional value and really help you scale your business?

This problem is particularly relevant for emerging exponential technologies such as artificial intelligence, machine learning and robotics. For those specific fields, highly specialized investors/advisors are essential for the success of the venture.

This is the reason why I wrote a long post on AI investors some time ago and why I am following up now with accelerators, which can be a valid investment alternative and business opportunity but that are commonly not fully understood.

But first, some fundamentals...


Image Credit: Jinning Li/Shutterstock

II. Who’s who in the funding game

 
Since the edges are blurring, it is hard to find a commonly shared definition for accelerators and incubators. Hence, I will provide two different definitions, one a bit more from a practitioner’s point of view, the other slightly more academic.

In the industry, the distinction between an accelerator and an incubator is simply related to the rationale for a company to join such a program. In other words, an incubator helps the entrepreneur in the development of her idea, while the accelerator focuses more on growing the business. The two programs have therefore two different goals and should be joined at a different stage of the startup lifecycle (Isabelle, 2013).

If we look instead at a more rigorous detailed academic definition, it would be worth to have a look at Cohen (2013) and Cohen and Hochberg (2014). They actually define a startup accelerator as

“a fixed-term, cohort-based program, including mentorship and educational components, that culminates in a public pitch event or demo day.”

From this definition is clear that the authors looked at different traits to characterize and distinguish different programs from each other. The key features can actually be summarized as follows.

Even though this academic definition clearly indicates thresholds and binary variables to identify different programs, it looks to me that — at least in the AI space — things are more complicated and actually it is really hard to define who is who (for help, check the brilliant review by Hausberg and Korreck, 2017). Furthermore, the important question we should ask is not whether to call a program accelerator or incubator, but rather what is the real value brought to the entrepreneur.

III. Are they worth their value?

 
If you are an entrepreneur, having so many different choices might make you wonder whether it might make sense to join one of those programs or not. And if you are an investor, a company, or anyone else looking at the space, you might start wondering if those programs suffer from an adverse selection problem: good companies go ahead with their feet while ‘lemon’ companies that cannot get funded or get the ball rolling go into these programs.

Entrepreneur Perspective: to join or not to join

Unless you are already an experienced entrepreneur, the short answer is yes, accelerators and incubators are worthy (Hallen et al., 2016). Starting and running a company is something no university can teach you (no matter how many innovation workshops you take or entrepreneurial courses you attend) but it is grounded on real life experience. In this respect, accelerator programs are sort of full-time educational bootcamp in which you rapidly learn what you need to at least survive the first year. Whether then you are gonna make it or not depends on how you transform that knowledge into the right actions.

Joining an accelerator is actually as reading a summary instead of the full book to do an exam: in this case, the full book would take you years to be read, while the summary takes a few months and can help you passing the exam. However, final graduation is a completely different thing.

‘Accelerators = Business synopsis’

Academic research, even if not unanimously (check this beautiful work by Yu, 2016), seems to confirm with data the value of those programs (Hochberg, 2015). Studies prove that accelerated companies reach milestones faster (Hallen et al., 2014), have a higher probability to raise further funding with respect to angel-supported startups (Winston-Smith and Hannigan, 2015), and that have even spillover effects on the entire entrepreneurial ecosystem (Fehder and Hochberg, 2015).

A warning though: even if some of those findings are true from a statistical point of view, there is a huge difference between different accelerators, and the quality of the program drastically impacts the positive effects for the startup.

Investor Perspective: should I stay or should I go

A good investor is basically the one who is able to:

  1. pick straight the winner and helping him become bigger and stronger;
  2. pick a potential winner with the right things in place and helping him become successful.

The first case requires a lot of ex-ante work (due diligence) but not much after you invest. You simply seat down, relax and wait (it is not that simple actually, but let me go with this narrative for a second). The problem here is that there are few companies with these traits and everyone wants to invest in them, which considerably reduces the risk-return tradeoff.

The second case is instead more interesting and shows the real skills and contribution of the investor. It is also what it happens, most of the time and with exceptions, with companies coming out from accelerators and incubators program. These are companies that, for whatever reasons (lack of previous experience, no access to funding, etc.), might not have made it by themselves but are now in the game. Think of big success stories as Dropbox, for example.

So the question is: as an investor, should I invest in companies coming out from accelerator programs? Or am I buying a lemon?

The answer is ‘simple’, once again: yes, but mainly in those ones coming from excellent successful programs.

The proliferation of accelerators and incubators program made really difficult for investors to find real value in accelerated companies, especially for AI-related technologies and businesses. Good companies join accelerators for learning, mentoring and to get more exposure, all things as an entrepreneur you want to get from the best ones out there. And if good companies join an accelerator, the accelerator becomes more successful and attract better and better companies and founders on the next batches. It is a virtuous circle, which is creating a clear polarization in the industry, a positive skew distribution where very few programs deliver excellent results while the majority of them do not add any value (and in some cases are even detrimental) to the participants. In other words, I think there is a strong adverse selection problem in the accelerators/incubators space.

Of course, this is not a law of nature and does not imply that every company coming out from Techstars is going to become a unicorn (or the other way round). It is simply a rule of thumb to allocate a bit more efficiently your capital. If you are then able to spot out a potential winner in a low-level accelerator, chapeau, give yourself a pat on the shoulder because you did a very nice job.

Accelerators Assessment Metrics: is the program any good?

The common denominator of the two perspectives is that everything comes back to how good an acceleration program is. I have no particular experience in setting up or participating in an accelerator, so I do not know for sure the problems or the metrics on how to assess it. This is my interpretation (quite general with some sprinkle of AI somewhere), but feel free to comment below and tell me more about different metrics and aspects I should also consider:

  1. Alumni network: who are the alumni of the program? This base represents the ‘customer base’ of the accelerator, so check it out if includes big names. Do not be trapped by average valuations of the portfolio of the program: having one Dropbox and dozen of ‘John Doe startups’ does not make it a good accelerator, it simply makes it a lucky one (look at different stats, if you want to, e.g., median, variance, etc.);
  2. Raising the next round: even though raising funds is not always a proof of business success, it is very often a good proxy for it. The more companies raise a further fund after the program, the better the program is;
  3. Raising a good next round: same considerations as above, with the additional aspect that companies need to raise a specific amount of money. The more companies can reach their funding goal, the better the program is.

    Be careful: evaluating an accelerator on the basis of the average amount of dollars raised is a huge mistake and only increments the already existing hype on AI;

  4. Survival rate: the accelerators are set to provide entrepreneurs with tools and network to survive for at least 12 months (this is my view). The higher number of companies are still operating after one year, the better the accelerator was;
  5. Exit: ceteris paribus, if companies coming out from programs are obtaining higher valuation than their competitors, shortening the time-to-exit, or simply increasing the probability of an exit, it means that the accelerator did the job it was supposed to.
     
    However, this point is controversial for at least two reasons: first, it is statistically hard to understand how an accelerator affects a final exit. Life is much more complicated than linking straight accelerator → higher exit, but if all the companies coming out from a specific program obtain higher valuations with respect to their peers, we know for sure that there is some endogeneity there, even if we might not be able to identify the specific factors that make a business more successful.
     
    Second, it depends on your view about business and what it means starting a company. Real visionary entrepreneurs do not start a company to sell it — they start something as it should run forever. An exit is somehow a defeat for some of them (there are exceptions, e.g., DeepMind), but the reality is that this class of entrepreneurs is disappearing. People start business nowadays with the idea in mind to sell out in 5 years to a specific buyer, or to use the technology developed to increase the salary base from $150k (a normal salary in big tech companies in the US for an AI researcher) to $7M (average amount got from acqui-hire in AI and machine learning sector).
     
    I am not saying this is wrong and this is certainly what an investor wants, but it can invalidate the ‘Exit’ metric as one variable to track for accelerators’ performance;

  6. Wider network: a good accelerator has top-level mentors and knows how to engage them to be effective. It also has people behind who can really understand AI technologies and can help entrepreneurs with latest developments in research, or partners that can provide datasets for feeding neural nets.


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